Schizophrenia is a common and debilitating condition with high personal costs to affected individuals and their families as well as high societal costs. Relatively little is known about the pathophysiology of schizophrenia. Although there is strong evidence for a genetic component to risk of schizophrenia, few specific genes involved in its etiology have been identified. In this set of coordinated R01s, we propose to take an alternative approach to localizing genes influencing risk of schizophrenia, combining established intermediate risk factors for schizophrenia with identification of novel transcriptional endophenotypes and combining standard GWAS gene localization approaches with innovative methods utilizing joint analysis of association and linkage and joint analysis of genomic and transcriptomic evidence. We will utilize existing samples and data from three ongoing studies: the Consortium on the Genetics of Schizophrenia (COGS); the Multiplex Multigenerational Investigation of Schizophrenia (MGI); and the Project among African Americans to Explore Risks for Schizophrenia (PAARTNERS). These three family studies were designed to investigate genetic influences on schizophrenia using neurocognitive phenotypes associated with schizophrenia risk. We hypothesize that alterations in gene regulation are responsible for some portion of the genetic liability to schizophrenia. Thus, we will use RNA expression levels both as potential endophenotypes for schizophrenia and as an alternative method of genome scanning. Identification of transcriptional correlates of schizophrenia will be facilitated by use of a novel Endophenotype Ranking Value (ERV) that combines the strength of the genetic signal on a potential endophenotype with the strength of its correlation with the disease of interest (i.e. schizophrenia) in a single measure. We will conduct a conventional genome-wide association study (GWAS) for schizophrenia, for newly identified transcriptional endophenotypes, and for classical neurocognitive risk factors. We will also take advantage of the large families in these samples to conduct joint linkage and association. Finally we will combine genomic and transcriptomic lines of evidence in a joint test to identify genes influencing schizophrenia and associated neurocognitive risk factors. All data generated in the course of the project will be shared through dbGaP and the NIMH Genetics Repository.